Overview

Dataset statistics

Number of variables21
Number of observations10049
Missing cells0
Missing cells (%)0.0%
Duplicate rows14
Duplicate rows (%)0.1%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Numeric16
Categorical4
Text1

Alerts

Dataset has 14 (0.1%) duplicate rowsDuplicates
amt_credit is highly overall correlated with amt_goods_priceHigh correlation
amt_goods_price is highly overall correlated with amt_creditHigh correlation
credit_agency_3_rating is highly overall correlated with days_creditHigh correlation
days_credit is highly overall correlated with credit_agency_3_ratingHigh correlation
remaining_installment is highly overall correlated with buro_max_amt_instalmentHigh correlation
buro_avg_amt_instalment is highly overall correlated with buro_max_amt_instalmentHigh correlation
buro_max_amt_instalment is highly overall correlated with remaining_installment and 1 other fieldsHigh correlation
days_birth is highly overall correlated with days_employedHigh correlation
days_employed is highly overall correlated with days_birthHigh correlation
name_contract_type is highly imbalanced (54.7%)Imbalance
target is highly imbalanced (60.7%)Imbalance
sk_id_curr is uniformly distributedUniform
credit_agency_1_rating has 5677 (56.5%) zerosZeros
credit_agency_3_rating has 2007 (20.0%) zerosZeros
days_credit has 1452 (14.4%) zerosZeros
days_credit_enddate has 1523 (15.2%) zerosZeros
remaining_installment has 556 (5.5%) zerosZeros
buro_avg_amt_instalment has 475 (4.7%) zerosZeros
buro_max_amt_instalment has 475 (4.7%) zerosZeros

Reproduction

Analysis started2023-09-04 05:12:52.109216
Analysis finished2023-09-04 05:13:27.893561
Duration35.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

sk_id_curr
Real number (ℝ)

UNIFORM 

Distinct10000
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1004975.2
Minimum1000000
Maximum1009999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:28.009449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile1000453.4
Q11002463
median1004975
Q31007487
95-th percentile1009496.6
Maximum1009999
Range9999
Interquartile range (IQR)5024

Descriptive statistics

Standard deviation2900.6641
Coefficient of variation (CV)0.0028863041
Kurtosis-1.2005964
Mean1004975.2
Median Absolute Deviation (MAD)2512
Skewness0.00044201906
Sum1.0098996 × 1010
Variance8413852
MonotonicityNot monotonic
2023-09-04T10:43:28.209116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000017 5
 
< 0.1%
1000014 5
 
< 0.1%
1000015 5
 
< 0.1%
1000016 5
 
< 0.1%
1000018 5
 
< 0.1%
1000019 5
 
< 0.1%
1000020 5
 
< 0.1%
1000027 4
 
< 0.1%
1000021 4
 
< 0.1%
1000022 4
 
< 0.1%
Other values (9990) 10002
99.5%
ValueCountFrequency (%)
1000000 1
< 0.1%
1000001 1
< 0.1%
1000002 1
< 0.1%
1000003 1
< 0.1%
1000004 1
< 0.1%
1000005 1
< 0.1%
1000006 1
< 0.1%
1000007 1
< 0.1%
1000008 1
< 0.1%
1000009 1
< 0.1%
ValueCountFrequency (%)
1009999 1
< 0.1%
1009998 1
< 0.1%
1009997 1
< 0.1%
1009996 1
< 0.1%
1009995 1
< 0.1%
1009994 1
< 0.1%
1009993 1
< 0.1%
1009992 1
< 0.1%
1009991 1
< 0.1%
1009990 1
< 0.1%

amt_credit
Real number (ℝ)

HIGH CORRELATION 

Distinct1833
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean593657.24
Minimum45000
Maximum2695500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:28.395496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45000
5-th percentile136270.8
Q1270000
median508496
Q3808650
95-th percentile1350000
Maximum2695500
Range2650500
Interquartile range (IQR)538650

Descriptive statistics

Standard deviation399553.26
Coefficient of variation (CV)0.67303695
Kurtosis1.6280948
Mean593657.24
Median Absolute Deviation (MAD)249786
Skewness1.2132242
Sum5.9656616 × 109
Variance1.5964281 × 1011
MonotonicityNot monotonic
2023-09-04T10:43:28.591573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 302
 
3.0%
225000 286
 
2.8%
675000 275
 
2.7%
180000 244
 
2.4%
270000 231
 
2.3%
900000 200
 
2.0%
545040 141
 
1.4%
808650 132
 
1.3%
254700 130
 
1.3%
135000 128
 
1.3%
Other values (1823) 7980
79.4%
ValueCountFrequency (%)
45000 9
0.1%
47970 3
 
< 0.1%
49500 1
 
< 0.1%
50940 16
0.2%
52128 4
 
< 0.1%
53910 1
 
< 0.1%
54504 1
 
< 0.1%
56880 1
 
< 0.1%
57339 1
 
< 0.1%
57564 1
 
< 0.1%
ValueCountFrequency (%)
2695500 1
 
< 0.1%
2517300 5
< 0.1%
2447940 2
 
< 0.1%
2428200 1
 
< 0.1%
2410380 1
 
< 0.1%
2395920 1
 
< 0.1%
2356920 1
 
< 0.1%
2295000 1
 
< 0.1%
2286210 1
 
< 0.1%
2254500 1
 
< 0.1%

amt_goods_price
Real number (ℝ)

HIGH CORRELATION 

Distinct366
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean533514.3
Minimum45000
Maximum2295000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:28.790785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45000
5-th percentile135000
Q1234000
median450000
Q3679500
95-th percentile1282500
Maximum2295000
Range2250000
Interquartile range (IQR)445500

Descriptive statistics

Standard deviation366402.95
Coefficient of variation (CV)0.68677251
Kurtosis2.1085236
Mean533514.3
Median Absolute Deviation (MAD)225000
Skewness1.3293503
Sum5.3612852 × 109
Variance1.3425112 × 1011
MonotonicityNot monotonic
2023-09-04T10:43:28.979563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225000 860
 
8.6%
450000 816
 
8.1%
675000 797
 
7.9%
900000 487
 
4.8%
270000 379
 
3.8%
180000 357
 
3.6%
454500 306
 
3.0%
1125000 281
 
2.8%
135000 273
 
2.7%
315000 177
 
1.8%
Other values (356) 5316
52.9%
ValueCountFrequency (%)
45000 39
0.4%
49500 2
 
< 0.1%
54000 5
 
< 0.1%
58500 11
 
0.1%
63000 1
 
< 0.1%
67500 48
0.5%
72000 4
 
< 0.1%
76500 9
 
0.1%
81000 17
 
0.2%
85500 5
 
< 0.1%
ValueCountFrequency (%)
2295000 1
 
< 0.1%
2254500 3
 
< 0.1%
2250000 24
0.2%
2245500 1
 
< 0.1%
2236500 1
 
< 0.1%
2182500 1
 
< 0.1%
2160000 1
 
< 0.1%
2070000 1
 
< 0.1%
1984500 4
 
< 0.1%
1930500 1
 
< 0.1%

credit_agency_1_rating
Real number (ℝ)

ZEROS 

Distinct4318
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21932555
Minimum0
Maximum0.929394
Zeros5677
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:29.182331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.45961
95-th percentile0.7713772
Maximum0.929394
Range0.929394
Interquartile range (IQR)0.45961

Descriptive statistics

Standard deviation0.28569641
Coefficient of variation (CV)1.3026134
Kurtosis-0.77322903
Mean0.21932555
Median Absolute Deviation (MAD)0
Skewness0.86309243
Sum2204.0024
Variance0.081622438
MonotonicityNot monotonic
2023-09-04T10:43:29.361610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5677
56.5%
0.72194 5
 
< 0.1%
0.115634 5
 
< 0.1%
0.565655 5
 
< 0.1%
0.437709 4
 
< 0.1%
0.561948 4
 
< 0.1%
0.765643 2
 
< 0.1%
0.544695 2
 
< 0.1%
0.689724 2
 
< 0.1%
0.42807 2
 
< 0.1%
Other values (4308) 4341
43.2%
ValueCountFrequency (%)
0 5677
56.5%
0.0244175 1
 
< 0.1%
0.0244626 1
 
< 0.1%
0.0298842 1
 
< 0.1%
0.0308563 1
 
< 0.1%
0.0340619 1
 
< 0.1%
0.035546 1
 
< 0.1%
0.0379431 1
 
< 0.1%
0.0384758 1
 
< 0.1%
0.0406839 1
 
< 0.1%
ValueCountFrequency (%)
0.929394 1
< 0.1%
0.929382 1
< 0.1%
0.92876 1
< 0.1%
0.926892 1
< 0.1%
0.926499 1
< 0.1%
0.924702 1
< 0.1%
0.922426 1
< 0.1%
0.921126 1
< 0.1%
0.918404 1
< 0.1%
0.918057 1
< 0.1%

credit_agency_2_rating
Real number (ℝ)

Distinct9290
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51410476
Minimum0
Maximum0.855
Zeros26
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:29.554668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1341016
Q10.391092
median0.565413
Q30.664276
95-th percentile0.7470802
Maximum0.855
Range0.855
Interquartile range (IQR)0.273184

Descriptive statistics

Standard deviation0.19115034
Coefficient of variation (CV)0.37181203
Kurtosis-0.25146416
Mean0.51410476
Median Absolute Deviation (MAD)0.119815
Skewness-0.79728141
Sum5166.2387
Variance0.036538451
MonotonicityNot monotonic
2023-09-04T10:43:29.737971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.285898 32
 
0.3%
0 26
 
0.3%
0.262258 15
 
0.1%
0.162145 14
 
0.1%
0.159679 14
 
0.1%
0.265256 12
 
0.1%
0.163187 11
 
0.1%
0.265312 9
 
0.1%
0.354225 9
 
0.1%
0.162192 9
 
0.1%
Other values (9280) 9898
98.5%
ValueCountFrequency (%)
0 26
0.3%
7.36 × 10-51
 
< 0.1%
0.000255576 1
 
< 0.1%
0.000262611 1
 
< 0.1%
0.000476216 1
 
< 0.1%
0.000698277 1
 
< 0.1%
0.00106354 1
 
< 0.1%
0.00120194 1
 
< 0.1%
0.0013404 1
 
< 0.1%
0.00175139 1
 
< 0.1%
ValueCountFrequency (%)
0.855 1
< 0.1%
0.815114 1
< 0.1%
0.809585 1
< 0.1%
0.805674 1
< 0.1%
0.803988 1
< 0.1%
0.803422 1
< 0.1%
0.802803 1
< 0.1%
0.8028 1
< 0.1%
0.802748 1
< 0.1%
0.802089 1
< 0.1%

credit_agency_3_rating
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct640
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40925672
Minimum0
Maximum0.885488
Zeros2007
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:29.924695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.172495
median0.461482
Q30.638044
95-th percentile0.780144
Maximum0.885488
Range0.885488
Interquartile range (IQR)0.465549

Descriptive statistics

Standard deviation0.26958743
Coefficient of variation (CV)0.6587245
Kurtosis-1.2234624
Mean0.40925672
Median Absolute Deviation (MAD)0.20917
Skewness-0.3048167
Sum4112.6208
Variance0.072677382
MonotonicityNot monotonic
2023-09-04T10:43:30.226528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2007
 
20.0%
0.626304 49
 
0.5%
0.694093 46
 
0.5%
0.67383 46
 
0.5%
0.7463 43
 
0.4%
0.000527265 41
 
0.4%
0.513694 40
 
0.4%
0.643026 40
 
0.4%
0.581484 40
 
0.4%
0.622922 39
 
0.4%
Other values (630) 7658
76.2%
ValueCountFrequency (%)
0 2007
20.0%
0.000527265 41
 
0.4%
0.0141483 1
 
< 0.1%
0.0145564 1
 
< 0.1%
0.0230618 1
 
< 0.1%
0.0238884 1
 
< 0.1%
0.0263597 1
 
< 0.1%
0.0318464 1
 
< 0.1%
0.032748 1
 
< 0.1%
0.0334404 1
 
< 0.1%
ValueCountFrequency (%)
0.885488 1
 
< 0.1%
0.88253 2
 
< 0.1%
0.881027 1
 
< 0.1%
0.880268 1
 
< 0.1%
0.869211 1
 
< 0.1%
0.865896 4
 
< 0.1%
0.863363 11
0.1%
0.86251 1
 
< 0.1%
0.861653 1
 
< 0.1%
0.859924 3
 
< 0.1%

days_credit
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6799
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-928.78932
Minimum-2922
Maximum0
Zeros1452
Zeros (%)14.4%
Negative8597
Negative (%)85.6%
Memory size78.6 KiB
2023-09-04T10:43:30.432734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2922
5-th percentile-2039.716
Q1-1361
median-931
Q3-423
95-th percentile0
Maximum0
Range2922
Interquartile range (IQR)938

Descriptive statistics

Standard deviation647.09939
Coefficient of variation (CV)-0.69671278
Kurtosis-0.38382204
Mean-928.78932
Median Absolute Deviation (MAD)463.67
Skewness-0.33464477
Sum-9333403.9
Variance418737.62
MonotonicityNot monotonic
2023-09-04T10:43:30.642482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1452
 
14.4%
-478 8
 
0.1%
-337 8
 
0.1%
-495 7
 
0.1%
-1331 7
 
0.1%
-840 7
 
0.1%
-532 6
 
0.1%
-180 6
 
0.1%
-900 6
 
0.1%
-1318 6
 
0.1%
Other values (6789) 8536
84.9%
ValueCountFrequency (%)
-2922 2
< 0.1%
-2913 1
< 0.1%
-2912 1
< 0.1%
-2904 1
< 0.1%
-2900 1
< 0.1%
-2899 1
< 0.1%
-2896 1
< 0.1%
-2888 1
< 0.1%
-2881 1
< 0.1%
-2877.5 1
< 0.1%
ValueCountFrequency (%)
0 1452
14.4%
-6 1
 
< 0.1%
-8 1
 
< 0.1%
-9 1
 
< 0.1%
-10 1
 
< 0.1%
-15 1
 
< 0.1%
-19 1
 
< 0.1%
-22 1
 
< 0.1%
-25 4
 
< 0.1%
-26 1
 
< 0.1%

days_credit_enddate
Real number (ℝ)

ZEROS 

Distinct7345
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean533.96407
Minimum-21342
Maximum31194
Zeros1523
Zeros (%)15.2%
Negative4803
Negative (%)47.8%
Memory size78.6 KiB
2023-09-04T10:43:30.826909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-21342
5-th percentile-1478.81
Q1-600.5
median0
Q3399.75
95-th percentile5376.48
Maximum31194
Range52536
Interquartile range (IQR)1000.25

Descriptive statistics

Standard deviation2986.4045
Coefficient of variation (CV)5.592894
Kurtosis41.125563
Mean533.96407
Median Absolute Deviation (MAD)532.333
Skewness5.3659052
Sum5365805
Variance8918611.6
MonotonicityNot monotonic
2023-09-04T10:43:31.026197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1523
 
15.2%
-342 6
 
0.1%
940 6
 
0.1%
-172 5
 
< 0.1%
-364.917 5
 
< 0.1%
-165 5
 
< 0.1%
30 5
 
< 0.1%
72 5
 
< 0.1%
645 5
 
< 0.1%
-610 5
 
< 0.1%
Other values (7335) 8479
84.4%
ValueCountFrequency (%)
-21342 1
< 0.1%
-6821.92 1
< 0.1%
-4630.2 1
< 0.1%
-4397.13 1
< 0.1%
-4015.15 1
< 0.1%
-2787 1
< 0.1%
-2751 1
< 0.1%
-2714 1
< 0.1%
-2687 1
< 0.1%
-2640 1
< 0.1%
ValueCountFrequency (%)
31194 1
< 0.1%
31187 1
< 0.1%
31185 1
< 0.1%
31153 1
< 0.1%
31150 1
< 0.1%
31149 1
< 0.1%
31084 1
< 0.1%
31081 1
< 0.1%
31080 1
< 0.1%
31069 1
< 0.1%

remaining_installment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4502
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7222651
Minimum0
Maximum57.5
Zeros556
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:31.207545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.65517
median6.64286
Q311.48
95-th percentile22.21904
Maximum57.5
Range57.5
Interquartile range (IQR)6.82483

Descriptive statistics

Standard deviation6.7255625
Coefficient of variation (CV)0.77107981
Kurtosis4.2523807
Mean8.7222651
Median Absolute Deviation (MAD)2.97253
Skewness1.7360969
Sum87650.042
Variance45.233191
MonotonicityNot monotonic
2023-09-04T10:43:31.387216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 556
 
5.5%
3 369
 
3.7%
6 314
 
3.1%
5 216
 
2.1%
4 92
 
0.9%
6.5 81
 
0.8%
5.5 76
 
0.8%
7 73
 
0.7%
12 63
 
0.6%
8 60
 
0.6%
Other values (4492) 8149
81.1%
ValueCountFrequency (%)
0 556
5.5%
0.0322581 1
 
< 0.1%
0.105263 1
 
< 0.1%
0.166667 3
 
< 0.1%
0.215686 1
 
< 0.1%
0.25 1
 
< 0.1%
0.265823 1
 
< 0.1%
0.291667 1
 
< 0.1%
0.3 1
 
< 0.1%
0.333333 1
 
< 0.1%
ValueCountFrequency (%)
57.5 1
< 0.1%
51 1
< 0.1%
50.1667 1
< 0.1%
48.5 1
< 0.1%
48.3333 1
< 0.1%
48 1
< 0.1%
47.525 1
< 0.1%
47 1
< 0.1%
46.5 1
< 0.1%
45.7407 1
< 0.1%

buro_avg_amt_instalment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9441
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17450.162
Minimum0
Maximum818597
Zeros475
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:31.593165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1701.182
Q17136.96
median11845.6
Q320451.6
95-th percentile49237.24
Maximum818597
Range818597
Interquartile range (IQR)13314.64

Descriptive statistics

Standard deviation22742.857
Coefficient of variation (CV)1.3033035
Kurtosis277.25901
Mean17450.162
Median Absolute Deviation (MAD)5799
Skewness11.342463
Sum1.7535668 × 108
Variance5.1723756 × 108
MonotonicityNot monotonic
2023-09-04T10:43:31.791291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
 
4.7%
49114.6 5
 
< 0.1%
15090.5 5
 
< 0.1%
18982.6 5
 
< 0.1%
6765.98 5
 
< 0.1%
9059.02 5
 
< 0.1%
7136.96 5
 
< 0.1%
7910.76 4
 
< 0.1%
11820.6 4
 
< 0.1%
33917.1 4
 
< 0.1%
Other values (9431) 9532
94.9%
ValueCountFrequency (%)
0 475
4.7%
0.189 1
 
< 0.1%
463.974 1
 
< 0.1%
567.846 1
 
< 0.1%
880.655 1
 
< 0.1%
900.791 1
 
< 0.1%
947.13 1
 
< 0.1%
972.433 1
 
< 0.1%
984.38 1
 
< 0.1%
1013.57 1
 
< 0.1%
ValueCountFrequency (%)
818597 1
< 0.1%
672121 1
< 0.1%
477748 1
< 0.1%
451231 1
< 0.1%
401670 1
< 0.1%
331834 1
< 0.1%
316875 1
< 0.1%
279494 1
< 0.1%
275445 1
< 0.1%
246309 1
< 0.1%

buro_max_amt_instalment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9229
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133648.66
Minimum0
Maximum3071840
Zeros475
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:31.978312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2552.882
Q112233.7
median30567.1
Q3121202
95-th percentile691000.6
Maximum3071840
Range3071840
Interquartile range (IQR)108968.3

Descriptive statistics

Standard deviation251938.95
Coefficient of variation (CV)1.8850839
Kurtosis14.412529
Mean133648.66
Median Absolute Deviation (MAD)23801.12
Skewness3.3590579
Sum1.3430354 × 109
Variance6.3473233 × 1010
MonotonicityNot monotonic
2023-09-04T10:43:32.191304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
 
4.7%
45000 10
 
0.1%
20250 9
 
0.1%
90000 9
 
0.1%
6750 9
 
0.1%
225000 8
 
0.1%
36000 7
 
0.1%
112500 7
 
0.1%
180000 6
 
0.1%
27000 6
 
0.1%
Other values (9219) 9503
94.6%
ValueCountFrequency (%)
0 475
4.7%
0.225 1
 
< 0.1%
952.155 1
 
< 0.1%
1574.32 1
 
< 0.1%
1597.32 1
 
< 0.1%
1707.93 1
 
< 0.1%
1893.82 1
 
< 0.1%
1906.52 1
 
< 0.1%
2088.41 1
 
< 0.1%
2094.57 1
 
< 0.1%
ValueCountFrequency (%)
3071840 1
< 0.1%
2425430 1
< 0.1%
2266320 1
< 0.1%
2211510 1
< 0.1%
2197130 1
< 0.1%
2059290 1
< 0.1%
2040610 1
< 0.1%
1989310 1
< 0.1%
1972400 1
< 0.1%
1966170 1
< 0.1%

occupation_type
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
Laborers
4727 
Sales staff
1046 
Core staff
890 
Managers
629 
Drivers
602 
Other values (15)
2155 

Length

Max length21
Median length8
Mean length9.4677082
Min length1

Characters and Unicode

Total characters95141
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaborers
2nd rowHigh skill tech staff
3rd rowDrivers
4th rowCore staff
5th rowSales staff

Common Values

ValueCountFrequency (%)
Laborers 4727
47.0%
Sales staff 1046
 
10.4%
Core staff 890
 
8.9%
Managers 629
 
6.3%
Drivers 602
 
6.0%
High skill tech staff 363
 
3.6%
0 348
 
3.5%
Accountants 315
 
3.1%
Medicine staff 264
 
2.6%
Security staff 208
 
2.1%
Other values (10) 657
 
6.5%

Length

2023-09-04T10:43:32.417986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
laborers 4804
33.8%
staff 3254
22.9%
sales 1046
 
7.4%
core 890
 
6.3%
managers 629
 
4.4%
drivers 602
 
4.2%
high 363
 
2.6%
skill 363
 
2.6%
tech 363
 
2.6%
0 348
 
2.4%
Other values (15) 1560
 
11.0%

Most occurring characters

ValueCountFrequency (%)
r 12917
13.6%
s 11304
11.9%
a 11101
11.7%
e 9784
10.3%
f 6508
 
6.8%
o 6440
 
6.8%
L 4881
 
5.1%
b 4848
 
5.1%
t 4694
 
4.9%
4173
 
4.4%
Other values (28) 18491
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80691
84.8%
Uppercase Letter 9790
 
10.3%
Space Separator 4173
 
4.4%
Decimal Number 366
 
0.4%
Dash Punctuation 77
 
0.1%
Other Punctuation 44
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 12917
16.0%
s 11304
14.0%
a 11101
13.8%
e 9784
12.1%
f 6508
8.1%
o 6440
8.0%
b 4848
 
6.0%
t 4694
 
5.8%
i 2739
 
3.4%
l 2092
 
2.6%
Other values (11) 8264
10.2%
Uppercase Letter
ValueCountFrequency (%)
L 4881
49.9%
S 1308
 
13.4%
C 1208
 
12.3%
M 893
 
9.1%
D 602
 
6.1%
H 380
 
3.9%
A 315
 
3.2%
P 91
 
0.9%
W 44
 
0.4%
R 42
 
0.4%
Other values (2) 26
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 348
95.1%
1 18
 
4.9%
Space Separator
ValueCountFrequency (%)
4173
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90481
95.1%
Common 4660
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 12917
14.3%
s 11304
12.5%
a 11101
12.3%
e 9784
10.8%
f 6508
7.2%
o 6440
7.1%
L 4881
 
5.4%
b 4848
 
5.4%
t 4694
 
5.2%
i 2739
 
3.0%
Other values (23) 15265
16.9%
Common
ValueCountFrequency (%)
4173
89.5%
0 348
 
7.5%
- 77
 
1.7%
/ 44
 
0.9%
1 18
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 12917
13.6%
s 11304
11.9%
a 11101
11.7%
e 9784
10.3%
f 6508
 
6.8%
o 6440
 
6.8%
L 4881
 
5.1%
b 4848
 
5.1%
t 4694
 
4.9%
4173
 
4.4%
Other values (28) 18491
19.4%
Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:32.613332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length12.116828
Min length1

Characters and Unicode

Total characters121762
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowOther
2nd rowTrade: type 3
3rd rowSelf-employed
4th rowPostal
5th rowSelf-employed
ValueCountFrequency (%)
type 3822
18.7%
business 2634
12.9%
entity 2634
12.9%
3 2384
11.7%
xna 1741
8.5%
self-employed 1286
 
6.3%
other 527
 
2.6%
2 481
 
2.4%
industry 474
 
2.3%
trade 402
 
2.0%
Other values (41) 4007
19.6%
2023-09-04T10:43:32.984484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13798
 
11.3%
10343
 
8.5%
t 9367
 
7.7%
s 9366
 
7.7%
y 8512
 
7.0%
n 8303
 
6.8%
i 7328
 
6.0%
p 5420
 
4.5%
u 3803
 
3.1%
r 3684
 
3.0%
Other values (42) 41838
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 86143
70.7%
Uppercase Letter 18498
 
15.2%
Space Separator 10343
 
8.5%
Decimal Number 4304
 
3.5%
Dash Punctuation 1286
 
1.1%
Other Punctuation 1188
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13798
16.0%
t 9367
10.9%
s 9366
10.9%
y 8512
9.9%
n 8303
9.6%
i 7328
8.5%
p 5420
 
6.3%
u 3803
 
4.4%
r 3684
 
4.3%
l 3260
 
3.8%
Other values (11) 13302
15.4%
Uppercase Letter
ValueCountFrequency (%)
T 3363
18.2%
B 2708
14.6%
E 2682
14.5%
A 1829
9.9%
S 1804
9.8%
N 1741
9.4%
X 1741
9.4%
O 527
 
2.8%
I 493
 
2.7%
M 474
 
2.6%
Other values (8) 1136
 
6.1%
Decimal Number
ValueCountFrequency (%)
3 2389
55.5%
2 497
 
11.5%
1 497
 
11.5%
0 294
 
6.8%
7 264
 
6.1%
4 204
 
4.7%
9 112
 
2.6%
6 25
 
0.6%
5 21
 
0.5%
8 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
10343
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1286
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 104641
85.9%
Common 17121
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13798
13.2%
t 9367
 
9.0%
s 9366
 
9.0%
y 8512
 
8.1%
n 8303
 
7.9%
i 7328
 
7.0%
p 5420
 
5.2%
u 3803
 
3.6%
r 3684
 
3.5%
T 3363
 
3.2%
Other values (29) 31697
30.3%
Common
ValueCountFrequency (%)
10343
60.4%
3 2389
 
14.0%
- 1286
 
7.5%
: 1188
 
6.9%
2 497
 
2.9%
1 497
 
2.9%
0 294
 
1.7%
7 264
 
1.5%
4 204
 
1.2%
9 112
 
0.7%
Other values (3) 47
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13798
 
11.3%
10343
 
8.5%
t 9367
 
7.7%
s 9366
 
7.7%
y 8512
 
7.0%
n 8303
 
6.8%
i 7328
 
6.0%
p 5420
 
4.5%
u 3803
 
3.1%
r 3684
 
3.0%
Other values (42) 41838
34.4%

code_gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
F
6616 
M
3433 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10049
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 6616
65.8%
M 3433
34.2%

Length

2023-09-04T10:43:33.161231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T10:43:33.292432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 6616
65.8%
m 3433
34.2%

Most occurring characters

ValueCountFrequency (%)
F 6616
65.8%
M 3433
34.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10049
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 6616
65.8%
M 3433
34.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 10049
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 6616
65.8%
M 3433
34.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 6616
65.8%
M 3433
34.2%

days_birth
Real number (ℝ)

HIGH CORRELATION 

Distinct7418
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16019.442
Minimum7705
Maximum25160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:33.443456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7705
5-th percentile9379.6
Q112377
median15781
Q319581
95-th percentile23160.6
Maximum25160
Range17455
Interquartile range (IQR)7204

Descriptive statistics

Standard deviation4340.5368
Coefficient of variation (CV)0.27095431
Kurtosis-1.0307688
Mean16019.442
Median Absolute Deviation (MAD)3592
Skewness0.10857922
Sum1.6097937 × 108
Variance18840260
MonotonicityNot monotonic
2023-09-04T10:43:33.642700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13465 6
 
0.1%
9776 6
 
0.1%
18333 5
 
< 0.1%
17718 5
 
< 0.1%
20070 5
 
< 0.1%
17567 5
 
< 0.1%
15948 5
 
< 0.1%
14583 5
 
< 0.1%
8728 5
 
< 0.1%
12931 5
 
< 0.1%
Other values (7408) 9997
99.5%
ValueCountFrequency (%)
7705 1
< 0.1%
7708 1
< 0.1%
7711 1
< 0.1%
7714 1
< 0.1%
7736 1
< 0.1%
7738 1
< 0.1%
7742 1
< 0.1%
7747 1
< 0.1%
7763 1
< 0.1%
7766 1
< 0.1%
ValueCountFrequency (%)
25160 1
< 0.1%
25122 1
< 0.1%
25104 1
< 0.1%
25102 1
< 0.1%
25080 1
< 0.1%
25075 1
< 0.1%
25059 2
< 0.1%
25054 1
< 0.1%
25050 1
< 0.1%
25038 1
< 0.1%

days_id_publish
Real number (ℝ)

Distinct4424
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2981.7757
Minimum0
Maximum6228
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:33.943369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile368
Q11713
median3221
Q34294
95-th percentile4937
Maximum6228
Range6228
Interquartile range (IQR)2581

Descriptive statistics

Standard deviation1510.4789
Coefficient of variation (CV)0.50657025
Kurtosis-1.1209582
Mean2981.7757
Median Absolute Deviation (MAD)1204
Skewness-0.32758123
Sum29963864
Variance2281546.4
MonotonicityNot monotonic
2023-09-04T10:43:34.122654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4084 10
 
0.1%
4400 10
 
0.1%
3962 9
 
0.1%
1168 9
 
0.1%
4382 9
 
0.1%
4126 9
 
0.1%
4250 9
 
0.1%
4109 9
 
0.1%
4311 9
 
0.1%
4498 9
 
0.1%
Other values (4414) 9957
99.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
< 0.1%
3 3
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 3
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
6228 1
< 0.1%
6223 1
< 0.1%
6146 1
< 0.1%
6108 1
< 0.1%
6085 2
< 0.1%
6083 1
< 0.1%
6079 1
< 0.1%
6068 1
< 0.1%
6051 1
< 0.1%
6040 1
< 0.1%

customer_since
Real number (ℝ)

Distinct6669
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4979.8427
Minimum0
Maximum20981
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:34.319784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile314.4
Q11993
median4473
Q37477
95-th percentile11475.4
Maximum20981
Range20981
Interquartile range (IQR)5484

Descriptive statistics

Standard deviation3539.6387
Coefficient of variation (CV)0.71079327
Kurtosis-0.35179612
Mean4979.8427
Median Absolute Deviation (MAD)2693
Skewness0.59652032
Sum50042439
Variance12529042
MonotonicityNot monotonic
2023-09-04T10:43:34.499257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 8
 
0.1%
615 8
 
0.1%
34 7
 
0.1%
777 6
 
0.1%
3 6
 
0.1%
1021 6
 
0.1%
6051 6
 
0.1%
15 6
 
0.1%
108 6
 
0.1%
4110 6
 
0.1%
Other values (6659) 9984
99.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3
< 0.1%
2 2
 
< 0.1%
3 6
0.1%
4 3
< 0.1%
5 4
< 0.1%
6 1
 
< 0.1%
7 3
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
20981 1
< 0.1%
17959 1
< 0.1%
17313 1
< 0.1%
17298 1
< 0.1%
17252 1
< 0.1%
17013 1
< 0.1%
16988 1
< 0.1%
16970 1
< 0.1%
16591 1
< 0.1%
16533 1
< 0.1%

annual_income
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348092.35
Minimum100000
Maximum1040000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:34.681884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile120000
Q1220000
median340000
Q3460000
95-th percentile640000
Maximum1040000
Range940000
Interquartile range (IQR)240000

Descriptive statistics

Standard deviation161078.28
Coefficient of variation (CV)0.46274581
Kurtosis-0.85293252
Mean348092.35
Median Absolute Deviation (MAD)120000
Skewness0.31908177
Sum3.49798 × 109
Variance2.5946211 × 1010
MonotonicityNot monotonic
2023-09-04T10:43:34.846271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
420000 412
 
4.1%
380000 412
 
4.1%
460000 412
 
4.1%
440000 412
 
4.1%
400000 412
 
4.1%
100000 409
 
4.1%
140000 409
 
4.1%
120000 409
 
4.1%
280000 408
 
4.1%
300000 408
 
4.1%
Other values (21) 5946
59.2%
ValueCountFrequency (%)
100000 409
4.1%
120000 409
4.1%
140000 409
4.1%
160000 408
4.1%
180000 408
4.1%
200000 408
4.1%
220000 408
4.1%
240000 408
4.1%
260000 408
4.1%
280000 408
4.1%
ValueCountFrequency (%)
1040000 1
 
< 0.1%
680000 204
2.0%
660000 204
2.0%
640000 207
2.1%
620000 207
2.1%
600000 207
2.1%
580000 207
2.1%
560000 207
2.1%
540000 207
2.1%
520000 207
2.1%

days_employed
Real number (ℝ)

HIGH CORRELATION 

Distinct4281
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66560.975
Minimum17
Maximum365243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.6 KiB
2023-09-04T10:43:35.027800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile222
Q1928
median2228
Q35649
95-th percentile365243
Maximum365243
Range365226
Interquartile range (IQR)4721

Descriptive statistics

Standard deviation138458.67
Coefficient of variation (CV)2.080178
Kurtosis0.86935873
Mean66560.975
Median Absolute Deviation (MAD)1622
Skewness1.6934022
Sum6.6887124 × 108
Variance1.9170804 × 1010
MonotonicityNot monotonic
2023-09-04T10:43:35.224526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243 1777
 
17.7%
191 11
 
0.1%
203 10
 
0.1%
728 10
 
0.1%
152 10
 
0.1%
385 9
 
0.1%
570 9
 
0.1%
1188 9
 
0.1%
215 8
 
0.1%
1157 8
 
0.1%
Other values (4271) 8188
81.5%
ValueCountFrequency (%)
17 2
< 0.1%
22 1
< 0.1%
38 1
< 0.1%
42 1
< 0.1%
43 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
59 2
< 0.1%
63 2
< 0.1%
65 1
< 0.1%
ValueCountFrequency (%)
365243 1777
17.7%
15632 1
 
< 0.1%
15396 1
 
< 0.1%
14949 1
 
< 0.1%
14507 1
 
< 0.1%
14460 1
 
< 0.1%
14387 1
 
< 0.1%
14198 1
 
< 0.1%
13994 1
 
< 0.1%
13878 2
 
< 0.1%

name_contract_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
Cash loans
9094 
Revolving loans
955 

Length

Max length15
Median length10
Mean length10.475172
Min length10

Characters and Unicode

Total characters105265
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash loans
2nd rowRevolving loans
3rd rowCash loans
4th rowCash loans
5th rowCash loans

Common Values

ValueCountFrequency (%)
Cash loans 9094
90.5%
Revolving loans 955
 
9.5%

Length

2023-09-04T10:43:35.396128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T10:43:35.534096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
loans 10049
50.0%
cash 9094
45.2%
revolving 955
 
4.8%

Most occurring characters

ValueCountFrequency (%)
a 19143
18.2%
s 19143
18.2%
l 11004
10.5%
o 11004
10.5%
n 11004
10.5%
10049
9.5%
C 9094
8.6%
h 9094
8.6%
v 1910
 
1.8%
R 955
 
0.9%
Other values (3) 2865
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85167
80.9%
Space Separator 10049
 
9.5%
Uppercase Letter 10049
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 19143
22.5%
s 19143
22.5%
l 11004
12.9%
o 11004
12.9%
n 11004
12.9%
h 9094
10.7%
v 1910
 
2.2%
e 955
 
1.1%
i 955
 
1.1%
g 955
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 9094
90.5%
R 955
 
9.5%
Space Separator
ValueCountFrequency (%)
10049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 95216
90.5%
Common 10049
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 19143
20.1%
s 19143
20.1%
l 11004
11.6%
o 11004
11.6%
n 11004
11.6%
C 9094
9.6%
h 9094
9.6%
v 1910
 
2.0%
R 955
 
1.0%
e 955
 
1.0%
Other values (2) 1910
 
2.0%
Common
ValueCountFrequency (%)
10049
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 19143
18.2%
s 19143
18.2%
l 11004
10.5%
o 11004
10.5%
n 11004
10.5%
10049
9.5%
C 9094
8.6%
h 9094
8.6%
v 1910
 
1.8%
R 955
 
0.9%
Other values (3) 2865
 
2.7%

target
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.6 KiB
0
9271 
1
 
778

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10049
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9271
92.3%
1 778
 
7.7%

Length

2023-09-04T10:43:35.665870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T10:43:35.797232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9271
92.3%
1 778
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 9271
92.3%
1 778
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10049
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9271
92.3%
1 778
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 10049
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9271
92.3%
1 778
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9271
92.3%
1 778
 
7.7%

Interactions

2023-09-04T10:43:24.992384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:53.982582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:56.037222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.020967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:00.104310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:02.067693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:04.066253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:06.178734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:08.233573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:10.330380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:12.492517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:14.498737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:16.728090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:18.826283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:20.883153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:22.983636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:25.134601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:54.103714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:56.166905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.140908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:00.225173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:02.179033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:04.182900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:06.312807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:08.371544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:10.579599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:12.627818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:14.630581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:16.863162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:18.962719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:21.005850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:23.111937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:25.271563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:54.238676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:56.301857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.268362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:00.348070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:02.311221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:04.309842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:06.431588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:08.500668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:10.709300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:12.762243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:14.756249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:16.995847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:19.078250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:21.137667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:23.230713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:25.397346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:54.356322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:56.417388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.382273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:00.460188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:02.429926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:04.444303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:06.551599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:08.616533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:10.841063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:12.881052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:14.886968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:17.128278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:19.215631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:21.382340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:23.360855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:25.527150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:54.472135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:56.528669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.513905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:00.578412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:02.545738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:04.551952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:06.681694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:08.754420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:10.956291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:13.011879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:15.022651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:17.252374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:19.326641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:21.486509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:23.473366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:25.653568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:54.597749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:56.639802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.624529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:00.702139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:02.678375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:04.682240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:06.813653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:08.882306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:11.078704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:13.129095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:15.146571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:17.381932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:19.445583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:21.593912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:23.602576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:25.778017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:54.787241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:56.760149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.746246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:00.810837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:02.805299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:04.796942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:06.930548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:08.996611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:11.199132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:13.241449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:15.293463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:17.514053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:19.579982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:21.728365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:23.728024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:25.913103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:54.915601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:56.898823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.876626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:00.944057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:02.940111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:04.931500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:07.071908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:09.135749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:11.342077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:13.362298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:15.447758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:17.643231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:19.713465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:21.845286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:23.859252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:26.054774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:55.038419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:57.021958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:58.995979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:01.063229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:03.067849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:05.050745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:07.199269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:09.256520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:11.473646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:13.492815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:15.566503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:17.782243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:19.829102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:21.969247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:23.971454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:26.200329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:55.165070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:57.136312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:59.130281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:01.178594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:03.194144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:05.278326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:07.314203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:09.389380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:11.598415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:13.620686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:15.713288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:17.913423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:19.963879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:22.095443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:24.095157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:26.329448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:55.291000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:57.277493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:59.235096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:01.291827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:03.309212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:05.405317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:07.447988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:09.519281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:11.721423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:13.738455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:15.823636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:18.046436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:20.077774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:22.214679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:24.225836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:26.460083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:55.416690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:57.402561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:59.362938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:01.442126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:03.442276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:05.537112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:07.588518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:09.654061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:11.867170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:13.865405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:16.061542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:18.178387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:20.227365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:22.346581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:24.370781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:26.610184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:55.535424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:57.539344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:59.503539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:01.570538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:03.575345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:05.673641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:07.730466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:09.789316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:11.991606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:14.000286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:16.212133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:18.297611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:20.365637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:22.491045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:24.506923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:26.736104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:55.679864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:57.657891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:59.622838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:01.700677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:03.688836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:05.792007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:07.850942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:09.919731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:12.119506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:14.127919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:16.343120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:18.444113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:20.499674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:22.619305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:24.635344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:26.860293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:55.786714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:57.764507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:59.847435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:01.809834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:03.809222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:05.912487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:07.978161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:10.037289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:12.238212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:14.240376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:16.463925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:18.570608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:20.620440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:22.731124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:24.752533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:27.111800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:55.917640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:57.898174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:42:59.949702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:01.927456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:03.935244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:06.050183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:08.106814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:10.182674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:12.358479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:14.360883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:16.594867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:18.681084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:20.746422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:22.851145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T10:43:24.860831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-04T10:43:35.901475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
sk_id_curramt_creditamt_goods_pricecredit_agency_1_ratingcredit_agency_2_ratingcredit_agency_3_ratingdays_creditdays_credit_enddateremaining_installmentburo_avg_amt_instalmentburo_max_amt_instalmentdays_birthdays_id_publishcustomer_sinceannual_incomedays_employedoccupation_typecode_gendername_contract_typetarget
sk_id_curr1.000-0.000-0.0040.0010.002-0.007-0.002-0.004-0.009-0.0050.008-0.0010.0130.010-0.008-0.0060.0000.0000.0160.000
amt_credit-0.0001.0000.9840.0380.0470.010-0.0340.0060.0130.0630.0620.0250.0070.0050.0040.0160.0600.0300.2700.016
amt_goods_price-0.0040.9841.0000.0400.0530.014-0.0350.0040.0090.0620.0590.0240.0080.0030.0040.0160.0630.0460.2370.000
credit_agency_1_rating0.0010.0380.0401.0000.0870.030-0.0610.015-0.0120.0280.033-0.131-0.016-0.0730.002-0.0940.0220.0730.0000.104
credit_agency_2_rating0.0020.0470.0530.0871.0000.083-0.089-0.0020.0050.0800.0700.1050.0580.081-0.0150.0590.0000.0220.0000.168
credit_agency_3_rating-0.0070.0100.0140.0300.0831.000-0.534-0.240-0.0030.0150.0370.1590.1390.0700.0030.1450.0210.0220.0000.164
days_credit-0.002-0.034-0.035-0.061-0.089-0.5341.0000.488-0.015-0.011-0.053-0.172-0.200-0.0770.006-0.1370.0000.0080.0230.116
days_credit_enddate-0.0040.0060.0040.015-0.002-0.2400.4881.0000.0260.0080.022-0.173-0.142-0.102-0.013-0.1200.0000.0130.0120.083
remaining_installment-0.0090.0130.009-0.0120.005-0.003-0.0150.0261.0000.4330.5100.1330.0170.047-0.0120.1110.0150.0410.0080.031
buro_avg_amt_instalment-0.0050.0630.0620.0280.0800.015-0.0110.0080.4331.0000.7670.0780.006-0.010-0.0140.0630.0400.0000.0230.017
buro_max_amt_instalment0.0080.0620.0590.0330.0700.037-0.0530.0220.5100.7671.0000.0710.026-0.016-0.0170.0700.0160.0260.0290.001
days_birth-0.0010.0250.024-0.1310.1050.159-0.172-0.1730.1330.0780.0711.0000.2540.310-0.0030.5760.0410.0490.0510.095
days_id_publish0.0130.0070.008-0.0160.0580.139-0.200-0.1420.0170.0060.0260.2541.0000.1140.0050.2610.0250.0310.0190.068
customer_since0.0100.0050.003-0.0730.0810.070-0.077-0.1020.047-0.010-0.0160.3100.1141.0000.0060.2050.0110.0390.0170.048
annual_income-0.0080.0040.0040.002-0.0150.0030.006-0.013-0.012-0.014-0.017-0.0030.0050.0061.000-0.0160.0160.0000.0100.000
days_employed-0.0060.0160.016-0.0940.0590.145-0.137-0.1200.1110.0630.0700.5760.2610.205-0.0161.0000.1670.0530.0210.037
occupation_type0.0000.0600.0630.0220.0000.0210.0000.0000.0150.0400.0160.0410.0250.0110.0160.1671.0000.4550.0540.000
code_gender0.0000.0300.0460.0730.0220.0220.0080.0130.0410.0000.0260.0490.0310.0390.0000.0530.4551.0000.0000.021
name_contract_type0.0160.2700.2370.0000.0000.0000.0230.0120.0080.0230.0290.0510.0190.0170.0100.0210.0540.0001.0000.004
target0.0000.0160.0000.1040.1680.1640.1160.0830.0310.0170.0010.0950.0680.0480.0000.0370.0000.0210.0041.000

Missing values

2023-09-04T10:43:27.328700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-04T10:43:27.708451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

sk_id_curramt_creditamt_goods_pricecredit_agency_1_ratingcredit_agency_2_ratingcredit_agency_3_ratingdays_creditdays_credit_enddateremaining_installmentburo_avg_amt_instalmentburo_max_amt_instalmentoccupation_typeorganization_typecode_genderdays_birthdays_id_publishcustomer_sinceannual_incomedays_employedname_contract_typetarget
01002500545040.0450000.00.2184980.4434710.377404-771.00231.006.9090913072.2013072.2LaborersOtherF119294879461200001862Cash loans0
11002501157500.0157500.00.0000000.4471980.374021-436.8015870.500.000000.000.0High skill tech staffTrade: type 3F2152948317078140000264Revolving loans0
21002502112500.0112500.00.0000000.6545920.0000000.000.0013.1739012522.5019066.5DriversSelf-employedM166051417161160000781Cash loans0
310025031040460.0868500.00.0000000.6174540.477649-1431.63-1065.6312.272709923.9151598.1Core staffPostalF166271615671800002255Cash loans0
41002504900000.0900000.00.0000000.7017910.629674-1265.00-1021.000.000000.000.0Sales staffSelf-employedF18928246755072000001850Cash loans0
51002505343377.0283500.00.0000000.5922950.436507-695.758929.4310.367307716.9625180.0LaborersBusiness Entity Type 3M18604213510710220000120Cash loans0
61002506225000.0225000.00.8696500.0887620.612704-1224.33-407.009.8679210053.8073984.5LaborersXNAF2455939338769240000365243Cash loans0
71002507284400.0225000.00.0000000.6232890.303146-601.50-293.009.2381024603.6026704.8Sales staffSelf-employedF139911675937260000811Cash loans0
81002508553806.0495000.00.0000000.4790980.121408-639.001148.5018.6364035905.20566066.0DriversSelf-employedM19262280778122800001515Cash loans1
910025091350000.01350000.00.0000000.0999530.643026-1295.40-694.205.0769215477.3045709.5Core staffSelf-employedF17773128837383000005618Cash loans0
sk_id_curramt_creditamt_goods_pricecredit_agency_1_ratingcredit_agency_2_ratingcredit_agency_3_ratingdays_creditdays_credit_enddateremaining_installmentburo_avg_amt_instalmentburo_max_amt_instalmentoccupation_typeorganization_typecode_genderdays_birthdays_id_publishcustomer_sinceannual_incomedays_employedname_contract_typetarget
1003910074901113130.0972000.00.1830460.3834640.399676-518.003134.0000.000005159.5935776.2Sales staffSelf-employedM93541975477340000128Cash loans0
100401007491562982.0486000.00.0000000.0000000.000000-337.00-158.0008.3913024207.60175101.000F1039512953740360000829Cash loans1
100411007492298512.0270000.00.0000000.6687430.217629-964.25233.0006.0000014555.6015341.1Sales staffSelf-employedF19007253194643800002634Cash loans0
100421007493651596.0526500.00.5809700.4934640.644679-1295.673606.57022.6471022272.60529313.0Core staffTransport: type 2F2117046558867400000365243Cash loans0
1004310074941574530.01350000.00.0000000.6056570.706205-1422.56-817.4449.8666718539.4028007.1ManagersBusiness Entity Type 3F22191436812062420000365243Cash loans0
100441007495835380.0675000.00.0000000.4189650.0000000.000.0002.3333312930.4012933.3LaborersBusiness Entity Type 1M16404149216154400004086Cash loans1
100451007496337500.0337500.00.0000000.7140340.549596-953.00-435.00012.342103133.6319125.0Medicine staffMedicineF14888605160514600006166Revolving loans0
1004610074971125000.01125000.00.0000000.2252600.065993-925.5088.0003.3333315666.20177224.0LaborersUniversityF125183546664100000990Cash loans0
100471007498497520.0450000.00.0000000.7130680.558507-957.40-466.2007.5000010393.3025640.1LaborersPostalF17489101542541200005820Cash loans0
100481007499180000.0180000.00.7751530.5756630.477649-202.00164.0007.150009759.4539168.4Core staffSelf-employedM178341375106061400001265Revolving loans0

Duplicate rows

Most frequently occurring

sk_id_curramt_creditamt_goods_pricecredit_agency_1_ratingcredit_agency_2_ratingcredit_agency_3_ratingdays_creditdays_credit_enddateremaining_installmentburo_avg_amt_instalmentburo_max_amt_instalmentoccupation_typeorganization_typecode_genderdays_birthdays_id_publishcustomer_sinceannual_incomedays_employedname_contract_typetarget# duplicates
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11000015299772.0247500.00.1156340.3466340.678568-495.005441.0007.8750015090.5033850.70LaborersBusiness Entity Type 2M8728136834944000001157Cash loans05
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